The invention relates to weld quality control and, more particularly, to in-process inspection of automated welding processes.
Manufacturers of turbine rotors and similar devices may utilize gas-tungsten-arc welding of component parts. Such welding improves mechanical properties including the ability to join components having different base materials as is often desirable in high-performance machines. Welding a turbine rotor, however, can take dozens of hours, and in order for the weld operators to identify a potential problem (e.g., a pore, lack of fusion, large silicate island, etc.) before it is welded over on the next pass, the operators must pay careful attention to every pulse made by their torch. It is a difficult task for a human operator to pay such close attention for such a long period of time.
Even assuming the weld operators may be capable of paying such close attention, weld operators typically do not have the experience to know exactly what they are looking for. An experienced welder may see features in the shape of the molten pool, the sidewall wetting geometry, the solidified ripple pattern and the solidified bead geometry that comparative amateurs do not see.
Most weld defects—e.g., pores, inclusions, lack of fusion, etc.—cannot be detected until testing such as ultrasonic testing is conducted on a finished weld. Detecting weld defects at the time they are created would save significant costs and cycle time. It would thus be desirable to utilize a “tireless” computer to pay close attention to the millions of weld pulses over the course of several days that it takes to weld a turbine rotor. It would further be desirable to utilize a machine vision system that is properly trained to watch these critical welds.
BRIEF SUMMARY OF THE INVENTION
In an exemplary embodiment, a method of detecting weld defects in real time includes a step of conducting a mock-up welding operation in a learning phase. The mock-up welding operating includes the steps of welding a first part to a second part, capturing images of a weld molten pool, and capturing images of a weld ripple shape and fillet geometry. The captured images are correlated with a weld position, and weld testing is performed on a weld resulting from the mock-up welding operation. Any defects in the weld are characterized, and the characterized defects are correlated with deviations in the captured images. During a production weld operation, the first camera captures images of a production weld molten pool, and the second camera captures images of a product weld ripple shape and fillet geometry. The captured images are processed to compute an aggregate probability that a weld position corresponding to the captured images contains a defect based on the correlated characterized defects.
In another exemplary embodiment, a method of detecting weld defects in real time includes the steps of (a) correlating potential weld defects with images of a mock weld molten pool and images of a mock weld ripple shape and fillet geometry; (b) depositing weld metal into an annular groove in a production weld operation; (c) a first camera capturing images of a production weld molten pool during the production weld operation; (d) a second camera capturing images of a production weld ripple shape and fillet geometry during the production weld operation; and (e) processing the images captured in (c) and (d) and computing an aggregate probability that a weld position corresponding to the images captured in (c) and (d) contains a defect based on the potential defects correlated in (a).
In still another exemplary embodiment, a system for detecting weld defects in real time includes a welding torch that enables weld metal to be deposited into an annular groove in a production weld operation, and first and second cameras. The first camera is positioned adjacent the welding torch and adjacent a part to be welded and captures images of a production weld molten pool during the production weld operation. The second camera is positioned farther from the welding torch than the first camera and downstream from the welding torch and captures images of a production weld ripple shape and fillet geometry during the production weld operation. A processor receives the images captured by the first and second cameras. The processor communicates with a database that stores correlated potential weld defects with images of a mock weld molten pool and images of a mock weld ripple shape and fillet geometry. The processor is programmed to process the images captured by the first and second cameras and to compute an aggregate probability that a weld position corresponding to the images captured by the first and second cameras contains a defect based on the potential defects correlated in the database.